In this note, we address the problem of simulating electromyographic signals arising from muscles involved in facial expressions - markedly those conveying affective information -, by relying solely on facial landmarks detected on video sequences. We propose a method that uses the framework of Gaussian Process regression to predict the facial electromyographic signal from videos where people display non-posed affective expressions. To such end, experiments have been conducted on the OPEN EmoRec II multimodal corpus.
Virtual EMG via Facial Video Analysis / Boccignone, G.; Cuculo, V.; Grossi, G.; Lanzarotti, R.; Migliaccio, R.. - 10484:(2017), pp. 197-207. (Intervento presentato al convegno ICIAP International Conference on Image Analysis and Processing : September, 11-15 tenutosi a Catania nel 2017) [10.1007/978-3-319-68560-1_18].
Virtual EMG via Facial Video Analysis
V. Cuculo;
2017
Abstract
In this note, we address the problem of simulating electromyographic signals arising from muscles involved in facial expressions - markedly those conveying affective information -, by relying solely on facial landmarks detected on video sequences. We propose a method that uses the framework of Gaussian Process regression to predict the facial electromyographic signal from videos where people display non-posed affective expressions. To such end, experiments have been conducted on the OPEN EmoRec II multimodal corpus.
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